Do Americans Think the Digital Economy is Fair? Using Supervised Learning to Explore Evaluations of Predictive Automation

Lehoucq, Emilio

doi:10.6339/22-JDS1053

Journal of Data Science

Do Americans Think the Digital Economy is Fair? Using Supervised Learning to Explore Evaluations of Predictive Automation

Volume 20, Issue 3 (2022): Special Issue: Data Science Meets Social Sciences, pp. 381–399

Emilio Lehoucq

https://doi.org/10.6339/22-JDS1053

Pub. online: 20 June 2022 Type: Data Science In Action

Open Access

Received
2 December 2021

Accepted
26 May 2022

Published
20 June 2022

Abstract

Predictive automation is a pervasive and archetypical example of the digital economy. Studying how Americans evaluate predictive automation is important because it affects corporate and state governance. However, we have relevant questions unanswered. We lack comparisons across use cases using a nationally representative sample. We also have yet to determine what are the key predictors of evaluations of predictive automation. This article uses the American Trends Panel’s 2018 wave ($n=4,594$) to study whether American adults think predictive automation is fair across four use cases: helping credit decisions, assisting parole decisions, filtering job applicants based on interview videos, and assessing job candidates based on resumes. Results from lasso regressions trained with 112 predictors reveal that people’s evaluations of predictive automation align with their views about social media, technology, and politics.

Supplementary material

Supplementary Material

This article includes a replication file with an R project, unprocessed and processed data, and a table listing all the predictors used in the models, how they are measured, and their pre-processing. The online appendix referred to in the text is also available as a supplement online at the journal’s website.

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2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

algorithmic fairness artificial intelligence machine learning public understanding of science and technology

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